Robustness in Econometrics by Vladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh

By Vladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh

This e-book offers contemporary study on robustness in econometrics. powerful information processing ideas – i.e., options that yield effects minimally suffering from outliers – and their functions to real-life financial and monetary events are the focus of this publication. The ebook additionally discusses purposes of extra conventional statistical ideas to econometric problems.
Econometrics is a department of economics that makes use of mathematical (especially statistical) the way to learn monetary structures, to forecast monetary and monetary dynamics, and to advance suggestions for reaching fascinating fiscal functionality. In daily facts, we frequently come upon outliers that don't mirror the long term fiscal developments, e.g., unforeseen and abrupt fluctuations. As such, it is very important improve powerful facts processing recommendations which may accommodate those fluctuations.

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Kreinovich · O. Kosheleva University of Texas at El Paso, 500 W. edu O. edu © Springer International Publishing AG 2017 V. Kreinovich et al. 1007/978-3-319-50742-2_3 51 52 S. Sriboonchitta et al. possible decisions—and then, to use these predictions to select the decision for which the corresponding prediction is the most preferable. When we have the full knowledge of the situation, the problem of selecting the best decision becomes a straightforward optimization problem. In practice, however, we rarely have the full knowledge.

In this section we also provide another practical example that corresponds to a stochastic volatility model (that is inspired by the Heston [8] model) and we describe backward smoothing of the resulting estimates. 2 Linear State Space Models In this section we present two explicit examples of linear state space models: (1) a Gaussian one in Sect. 1 and (2) a non-Gaussian one in Sect. 2. Moreover, we present the Kalman filter technique that solves these models. 1 Gaussian Linear State Space Models and the Kalman Filter In many situations Gaussian linear state space models are studied.

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